Breast Cancer Classification with Missing Data Imputation

Chlioui, Imane; Idri, Ali; Abnane, Ibtissam; Gea, Juan Manuel Carillo de; Fernández-Alemán, José Luis

doi:10.1007/978-3-030-16187-3_2

Cited by 6 publications

(2 citation statements)

References 21 publications

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“… K-nearest neighbor (KNN): Considered as the most ML technique used for MD imputation [ 21 ], based on KNN algorithm. It consists of imputing the missing value considering the K closest instances according to a given distance metric [ 27 ]. Support Vector Regression (SVR): Considered as the regression model of Support Vector Machines (SVM) developed by Vapnik [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Chlioui

Abnane

Idri

2020

Lecture Notes in Computer Science

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Missing data imputation is an important task when dealing with crucial data that cannot be discarded such as medical data. This study evaluates and compares the impacts of two statistical and two machine learning imputation techniques when classifying breast cancer patients, using several evaluation metrics. Mean, Expectation-Maximization (EM), Support Vector Regression (SVR) and K-Nearest Neighbor (KNN) were applied to impute 18% of missing data missed completely at random in the two Wisconsin datasets. Thereafter, we empirically evaluated these four imputation techniques when using five classifiers: decision tree (C4.5), Case Based Reasoning (CBR), Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). In total, 1380 experiments were conducted and the findings confirmed that classification using imputation based machine learning outperformed classification using statistical imputation. Moreover, our experiment showed that SVR was the best imputation method for breast cancer classification.

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Section: Methodsmentioning

confidence: 99%

Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Chlioui

Abnane

Idri

2020

Lecture Notes in Computer Science

Self Cite

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“…Evaluation of the model was done with both RMSE ( Root Mean Square Error) and NMAE (Normalized Mean Absolute Error) and gave better accuracy as compared to the other existing models. In 2019, Ali Idri et al[23] used three classifiers C4.5, SVM, and MLP on two different datasets to predict the missing values. 162 experiments using KNN for MCAR, MAR, NMAR were conducted.…”

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confidence: 99%

Hybrid Missing Value Imputation Algorithm- KLR

Sharma¹

2022

MSEA

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The data mining process mainly deals with the estimation, prediction, pattern extraction, and classification in big databases. The presence of missing values in the dataset decreases the accuracy of data mining classifiers. Therefore it is necessary to deal with missing values in the dataset to achieve accurate results. To improve the quality of data and prediction accuracy in the classification process, the authors have proposed a new hybrid missing value prediction algorithm, KLR, by combining the KNN and linear regression approach. The proposed KLR algorithm has been used for class validation and missing values imputation. Wisconsin Breast Cancer Diagnostic Dataset of 569 instances with 32 attributes from the machine learning repository of UCI, Irvinewasused to conduct the study. The Pearson Coefficient Correlation method is used for feature selection.Data normalization is performed using Min-max scaling technique. The Scikit-learn library for machine learning in python is used to complete all the experiments as the experimental framework. The mean square error method is used to evaluate the performance of the model. The proposed KLR algorithm with 450 nearest neighbors out of 569 gives the lowest MSE ie 0.00188 and more accurately predicts the missing values as compared to the classic models.

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Machine Learning for Healthcare: Introduction

Gupta¹,

Sedamkar²

2020

Learning and Analytics in Intelligent Systems

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Breast Cancer Classification with Missing Data Imputation

Cited by 6 publications

References 21 publications

Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification

Hybrid Missing Value Imputation Algorithm- KLR

Machine Learning for Healthcare: Introduction

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